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Course Outline
Introduction to Multimodal AI
- Understanding multimodal data
- Key concepts and definitions
- History and evolution of multimodal learning
Multimodal Data Processing
- Data collection and preprocessing
- Feature extraction from different modalities
- Data fusion techniques
Multimodal Representation Learning
- Learning joint representations
- Cross-modal embeddings
- Transfer learning across modalities
Multimodal Alignment and Translation
- Aligning data from multiple modalities
- Cross-modal retrieval systems
- Translation between modalities (e.g., text-to-image, image-to-text)
Multimodal Reasoning and Inference
- Logic and reasoning with multimodal data
- Inference techniques in multimodal AI
- Applications in question answering and decision making
Generative Models in Multimodal AI
- Generative Adversarial Networks (GANs) for multimodal data
- Variational Autoencoders (VAEs) for cross-modal generation
- Creative applications of generative multimodal AI
Multimodal Fusion Techniques
- Early, late, and hybrid fusion methods
- Attention mechanisms in multimodal fusion
- Fusion for robust perception and interaction
Applications of Multimodal AI
- Multimodal human-computer interaction
- AI in autonomous vehicles
- Healthcare applications (e.g., medical imaging and diagnostics)
Ethical Considerations and Challenges
- Bias and fairness in multimodal systems
- Privacy concerns with multimodal data
- Ethical design and deployment of multimodal AI systems
Advanced Topics in Multimodal AI
- Multimodal transformers
- Self-supervised learning in multimodal AI
- The future of multimodal machine learning
Summary and Next Steps
Requirements
- Basic understanding of artificial intelligence and machine learning
- Proficiency in Python programming
- Familiarity with data handling and preprocessing
Audience
- AI researchers
- Data scientists
- Machine learning engineers
21 Hours
